A focused wedge into defense decision intelligence.
Praevian is building JLAS-AI to solve one measurable problem well: choosing among constrained resource options when the consequences are operational. Defense sustainment is the beachhead; the same decision exists across emergency management, aviation MRO and critical infrastructure.
Why this, why now, why us.
The decision gap
Defense and industrial organizations have invested in visibility. The gap is the decision that follows: allocating scarce parts, lift and maintenance capacity when priorities compete.
Pressure is rising
Contested-logistics planning compresses timelines and removes slack, increasing demand for decision support that is explainable and auditable rather than a black box.
Operator-founded
23 years of Army logistics and mobility experience combined with graduate computer science. Domain logic is the hardest part to copy, and it's built in from day one.
A narrow wedge with room to scale.
Constrained allocation → mission effect
A specific, measurable decision problem that can be evaluated without rebuilding a customer's enterprise stack.
Decision architecture + domain logic
Value compounds in resource-to-consequence models, constraint handling, decision boundaries and outcome feedback from every evaluation.
Evaluate → Pilot → Integrate → Expand
Start with one organization, one workflow and one measurable outcome, then expand by program and into dual-use markets.
How Praevian makes money.
Early revenue comes from funded evaluations and pilots through government innovation programs and contract vehicles. As JLAS transitions into programs of record and enterprise customers, revenue shifts to recurring software licenses priced per program, site or decision workflow, with integration support delivered alongside prime and technology partners.
Fund milestones, not overhead.
Capital goes to the work that produces evidence. Headcount, facilities and broad marketing follow validated milestones.
Product & engineering
Optimization, integration adapters and deployment hardening.
Security & infrastructure
Accreditation-ready environments as sponsor requirements grow.
Mission validation
Sponsor evaluations, pilot support and evidence capture.
Corporate operations
Controlled and milestone-driven.
Where we are and what's next.
TRL 4 demonstrator
Decision behavior and controls proven on synthetic scenarios.
First sponsor evaluation
One bounded workflow with predefined success measures.
Funded pilot
Integration and security evidence in the user environment.
Transition
Program adoption and first dual-use customer.
JLAS-AI is currently a controlled demonstrator using synthetic, unclassified data. It has no ATO and no autonomous execution authority.
Let's talk.
We're speaking with investors focused on defense technology, dual-use software and disciplined product development. We'll share the deck, technical overview and validation roadmap.